Haze transfer and feature aggregation network for real-world single image dehazing

The absence of ground truth for hazy images in real scenes results in most dehazing models being trained only by synthesized datasets, which is not feasible for real hazy images due to the domain shift. To address this problem, a haze transfer and feature aggregation network, which includes a haze transfer subnetwork and a feature aggregation-based dehazing subnetwork, is developed in this study. In the haze transfer subnetwork, a discriminator group is designed to transfer haze information from real hazy images to clear images. The generated hazy image and its corresponding clear image form a training sample pair to solve the domain shift problem. The deep–shallow feature correlation has been considered by feature aggregation in the dehazing subnetwork. Information related to the haze-free image is highlighted, and the guidance of clear images can be fully used to prevent detailed information loss during feature extraction. The proposed method provides a new aspect of usage for real hazy images during training and improves the scalability of dehazing models. The experimental results show that the dehazing performance of the proposed method outperforms the state-of-the-art dehazing methods compared. The algorithm code is available at https://github.com/lhf12278/HTFA-Net.